zhengruifeng commented on code in PR #37995:
URL: https://github.com/apache/spark/pull/37995#discussion_r981914861
##########
python/pyspark/pandas/series.py:
##########
@@ -6442,6 +6445,8 @@ def argmin(self, axis: Axis = None, skipna: bool = True)
-> int:
raise ValueError("axis can only be 0 or 'index'")
sdf = self._internal.spark_frame.select(self.spark.column,
NATURAL_ORDER_COLUMN_NAME)
seq_col_name = verify_temp_column_name(sdf,
"__distributed_sequence_column__")
+
+ cached = sdf.cache()
Review Comment:
hmm, the case maybe more complex:
1. in case an action is triggered and then a scalar is retuned, like
`argmin`, we can explicitly persist the datasets and unpersist it after
computation;
2. otherwise, (the most cases) another `DataFrame/Series` is returned, and
we can not unpersist the cached datasets;
I am wondering if there is significant regression if we do not
localcheckpoint or cache the internal dataset? indexing operations easily
invoke `attach_distributed_sequence_column`/`attach_default_index`
1. if the dataset is small, recomputation maybe not a big deal;
2. if the dataset is large (like 1 billion rows, or/and 100+columns), it may
consume too many memory to persist the internal datasets;
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
